Identification of hub genes and pathways associated with hepatocellular carcinoma based on network strategy

The objective of this study was to identify hub genes and pathways associated with hepatocellular carcinoma (HCC) by centrality analysis of a co-expression network. A co-expression network based on differentially expressed (DE) genes of HCC was constructed using the Differentially Co-expressed Genes and Links (DCGL) package. Centrality analyses, for centrality of degree, clustering coefficient, closeness, stress and betweenness for the co-expression network were performed to identify hub genes, and the hub genes were combined together to overcome inconsistent results. Enrichment analyses were conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Finally, validation of hub genes was conducted utilizing reverse transcription-polymerase chain reaction (RT-PCR) analysis. In total, 260 DE genes between normal controls and HCC patients were obtained and a co-expression network with 154 nodes and 326 edges was constructed. From this, 13 hub genes were identified according to degree, clustering coefficient, closeness, stress and betweenness centrality analysis. It was found that reelin (RELN), potassium voltage-gated channel subfamily J member 10 (KCNJ10) and neural cell adhesion molecule 1 (NCAM1) were common hub genes across the five centralities, and the results of RT-PCR analysis for RELN, KCNJ10 and NCAM1 were consistent with the centrality analyses. Pathway enrichment analysis of DE genes showed that cell cycle, metabolism of xenobiotics by cytochrome P450 and p53 signaling pathway were the most significant pathways. This study may contribute to understanding the molecular pathogenesis of HCC and provide potential biomarkers for its early detection and effective therapies.